This specification relates to processing local search results.
The Internet provides access to a wide variety of resources such as video or audio files, web pages for particular subjects, book articles, or news articles. A search system can identify resources in response to a search query that includes one or more search phrases (i.e., one or more words). The search system ranks the resources based on their relevance to the search query and on measures of quality of the resources and provides search results that link to the identified resources. The search results are typically ordered for viewing according to the rank.
Some search systems can obtain or infer a location of a user device from which a search query was received and include local search results that are responsive to the search query. A local search result is a search result that references a local document. A local document, in turn, is a document that has been classified as having local significance to particular locations of user devices. Accordingly, a local document may receive a search score “boost” for a query if the location associated with the local document is near the location of the user device. For example, in response to a search query for “coffee shop,” the search system may provide local search results that reference web pages for coffee shops near the location of the user device. Many users in various geographic regions will likely be satisfied with receiving local results for coffee shops in response to the search query “coffee shop” because it is likely that a user submitting the query “coffee shop” is interested in search results for coffee shops that are local to the user's location.
Some local results, however, may have very high scores independent of the local scoring boost. This score, which is referred to as a “location independent score,” may be high due to the document being of interest to people outside of a region that includes the location of the local result. For example, a restaurant may have a very unique name, and may also be a famous restaurant nationwide. Accordingly, although the restaurant has local significant, it may still be of very great interest to users nationwide. Thus, a search algorithm that takes into account search traffic and query log data may rank a document for the restaurant (e.g., a web page) very high.
By way of another example, a power company named after Thomas Edison may have very heavy local traffic in a large state. Thus, the search algorithm that takes into account search traffic and query log data will also rank a document for the power company very high due to the sheer volume or traffic to the webpage by its many customers. However, the power company webpage may be of very little interest to a user several states away. For example, a user may desire to learn about Thomas Edison, and not the power company that bears his name. However, due to the large volume of traffic to the power company web page, that web page may be ranked higher than a webpage about the man, the latter of which is more likely to be of interest to a user that inputs the query “Edison.”